4 research outputs found
Pengaruh Pelatihan dan Hypnomotivation terhadap Kinerja Karyawan
No full textIn the midst of rapid changes in the business environment, employee training has been recognized as an effective strategy to improve individuals’ skills, knowledge, and abilities in adapting to increasingly complex job demands. Meanwhile, high motivation is also an essential factor in determining employee performance. Deep and sustained motivation, known as hypnomotivation, has been proven to contribute to individual productivity and creativity. The objectives of this study are: (1) To determine whether training and hypnomotivation have a significant simultaneous effect on employee performance among AS Academy training participants; (2) To determine whether training has a significant partial effect on employee performance among AS Academy training participants; and (3) To determine whether hypnomotivation has a significant partial effect on employee performance among AS Academy training participants. This study employed a quantitative descriptive statistical method, with data collected through questionnaires distributed via Google Forms to 109 participants and analyzed using IBM SPSS. The results show that: (1) Training and hypnomotivation have a significant simultaneous effect on employee performance among AS Academy training participants; (2) Training has a significant partial effect on employee performance; and (3) Hypnomotivation has a significant partial effect on the performance of AS Academy training participants
Hukum dan Mitigasi Permasalahan dalam Perjanjian Pengikatan Jual Beli (PPJB)
No full textPenelitian ini bertujuan untuk menganalisis ketentuan hukum yang mengatur Perjanjian Pengikatan Jual Beli (PPJB) serta mengidentifikasi langkah-langkah mitigasi yang dapat dilakukan untuk mengatasi permasalahan yang timbul dalam implementasinya. PPJB sering digunakan sebagai bentuk pengikatan awal sebelum transaksi jual beli dilakukan secara sah di hadapan hukum, namun praktiknya sering menimbulkan konflik hukum akibat kurangnya pemahaman para pihak mengenai aspek legal yang mengatur perjanjian ini. Penelitian ini menggunakan metode yuridis normatif dengan pendekatan peraturan perundang-undangan dan studi kasus. Hasil penelitian menunjukkan bahwa meskipun PPJB telah diatur dalam beberapa ketentuan hukum di Indonesia, implementasinya masih menghadapi berbagai tantangan, seperti kurangnya perlindungan hukum bagi pembeli dan ketidakjelasan klausul dalam perjanjian. Oleh karena itu, mitigasi permasalahan ini dapat dilakukan dengan penguatan klausul kontrak, pendampingan hukum bagi pihak yang terlibat, serta pengawasan yang lebih ketat oleh otoritas terkait. Penelitian ini memberikan rekomendasi bagi para pihak dan pembuat kebijakan untuk meningkatkan kepastian hukum dalam transaksi PPJB
KLASIFIKASI ALZHEIMER PADA CITRA MRI OTAK DENGAN CONVOLUTIONAL NEURAL NETWORK
No full textIn deep learning, Convolutional Neural Network (CNN) is an algorithm from Artificial Neural Network (ANN) which is generally used to analyze visual images. This algorithm can automatically extract important features from each image without human assistance, besides that the CNN algorithm is also more efficient than other neural network methods, especially in memory and complexity. In training, the algorithm will be given training data in the form of images that have been labeled so that the algorithm will be able to recognize the important characteristics of each of the labeled images. After the training stage, the trained algorithm will be given data validation in the form of an unlabeled image to be analyzed and classified. The algorithm will analyze the training and validation data for the specified number of epochs and provide information in the form of the level of accuracy of each epoch that is performed. Some that affect the level of accuracy include the type of optimizer, the pixel size of the input image, and the number of epochs. In this study, the CNN algorithm was used with a layer sequence made personally by the author. The research was conducted in a cloud-based Jupyter notebook environment called Google Colab. The dataset used in this study is the Alzheimer\u27s MRI Preprocessed Dataset which can be accessed by the public on the Kaggle website. The dataset consists of 6400 brain MRI scan images which are divided into four classes, namely: Non Demented, Very Mild Demented, Mild Demented, and Moderate Demented. As much as 20% of the dataset is used as data validation. In this study, the dataset will be analyzed by the CNN algorithm with several predetermined scenarios, then the accuracy of the training and validation data will be compared with each other to find the most optimal scenario. There are two input image pixel size scenarios to be compared, namely 128 x 128 pixels and 224 x 224 pixels. There are three types of optimizers that will be compared, namely Stochastic Gradient Descent (SGD), Adam, and RMSprop. From the research results, the most optimal type of optimizer to use with the architecture that has been made and the Alzheimer\u27s MRI Preprocessed Dataset is the Adam optimizer. Architectural training with an input size scenario of 224 x 224 pixels, seven epochs, and using the Adam optimizer achieves the most optimal accuracy rate, namely with a training data accuracy rate of 93.01% and a data validation accuracy rate of 94.45%. Architecture training with an input size scenario of 224 x 224 pixels and using the Adam optimizer achieves the most optimal number of epochs, namely achieving an accuracy level above 90% in just five epochs.
Keywords: CNN, Alzheimer\u27s, accuracy, optimizer, optimal.
Daftar Pustaka
[1] Burns, A., & Iliffe, S. (2009). Alzheimer\u27s disease. Bmj-British Medical Journal, 338.
[2] Dementia. (2022, 20 September). https://www.who.int/news-room/factsheets/detail/dementia
[3] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
[4] Khan, S., Barve, K. H., & Kumar, M. S. (2020). Recent advancements in pathogenesis, diagnostics and treatment of Alzheimer’sdisease. Current Neuropharmacology, 18(11), 1106-1125.
[5] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
[6] Mendez, M. F. (2006). The accurate diagnosis of early-onset dementia. The International Journal of Psychiatry in Medicine, 36(4), 401-412.
[7] Mortimer, J. A., Borenstein, A. R., Gosche, K. M., & Snowdon, D. A. (2005). Very early detection of Alzheimer neuropathology and the role of brain reserve in modifying its clinical expression. Journal of geriatric psychiatry and neurology, 18(4), 218-223.
[8] National Institute for Health and Clinical Excellence. (2006, November). Dementia: Quick Reference Guide. Diambil kembali darihttps://web.archive.org/web/20080227161412/http://www.nice.org.uk/nicemedia/pdf/CG042quickrefguide.pdf.
[9] Simon, R. P., Aminoff, M. J., & Greenberg, D. A. (2009). Clinical neurology. Lange Medical Books/McGraw-Hill.
[10] Smith, M. A. (1998). Alzheimer disease. International review of neurobiology, 42, 1-54.
[11] Valueva, M. V., Nagornov, N. N., Lyakhov, P. A., Valuev, G. V., & Chervyakov, N. I. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and computers in simulation, 177, 232-243
LAPORAN INDIVIDU PRAKTIK LAPANGAN TERBIMBING (PLT)
No full textMagang III terintegrasi dengan mata kuliah Praktik Lapangan Terbimbing
(PLT) mempunyai kegiatan yang terkait dengan pembelajaran maupun kegiatan yang
mendukung berlangsungnya pembelajaran. Mata kuliah PLT diharapkan dapat
memberikan pengalaman belajar bagi mahasiswa, terutama dalam hal pengalaman
mengajar, memperluas wawasan, pelatihan dan pengembangan kompetensi yang
diperlukan dalam bidangnya, peningkatan keterampilan, kemandirian, tanggung
jawab, dan kemampuan dalam memecahkan masalah.
Secara umum, pelaksanaan PLT meliputi empat tahapan yaitu tahap
persiapan, pelaksanaan, evaluasi dan penyusunan laporan. Tahapan pelaksanaan PLT
meliputi tahap pembekalan, penerjunan, dan praktik mengajar. Pelaksanaan program
PLT dimulai dari tanggal 15 September 2017 sampai dengan 15 November 2017
yang diisi dengan observasi kelas dan lembaga, konsultasi, pembuatan Rencana
Pelaksanaan Pembelajaran, pembuatan materi ajar dan media pembelajaran, praktik
mengajar, dan evaluasi. Dalam praktik mengajar, kelas yang diampu adalah kelas X
Teknik Pemesinan 1 dan X Teknik Pemesinan 2. Mata pelajaran yang diampu adalah
teknologi mekanik dan praktik kerja bangku.
Penyelenggaraan PLT untuk mendukung pengembangan kompetensi
mahasiswa sebagai calon guru atau tenaga pendidik. Melalui program ini, praktikan
diharapkan memiliki keterampilan dalam mengelola kelas sehingga kegiatan
pembelajaran dapat berjalan dengan baik dan menghasilkan lulusan yang
berkompeten. Pelaksanaan PLT di SMK Muhammadiyah 1 Bantul ini juga
diharapkan dapat menjadi salah satu fungsi kehumasan mahasiswa sehingga sekolah
dapat menjadi mitra Universitas Negeri Yogyakarta untuk melaksanakan PLT tahun berikutnya
